Probabilistic Prediction of Longitudinal Driving Behaviour for Driving Simulator Pre-Positioning

@inproceedings{Eppink2023,
title = {Probabilistic Prediction of Longitudinal Driving Behaviour for Driving Simulator Pre-Positioning},
author = {Jesse Eppink and Maurice Kolff and Joost Venrooij and Daan M. Pool and Max Mulder},
editor = {Andras Kemeny and Jean-Rémy Chardonnet and Florent Colombet},
isbn = {978-2-9573777-3-2},
year  = {2023},
date = {2023-09-08},
urldate = {2023-09-08},
booktitle = {Proceedings of the Driving Simulation Conference 2023 Europe VR},
pages = {119-126},
address = {Antibes Juan-les-Pins, France},
organization = {Driving Simulation Association},
abstract = {Due to the non-deterministic nature of longitudinal human driver behaviour, motion cueing algorithms currently cannot fully utilize the workspace of driving simulators. This paper explores the possibility of using various predictor variables to predict longitudinal driving behaviour. Through the development of a logistic regression model, it is shown that a combination of the current vehicle velocity, the speed limit eight seconds ahead and the accelerator pedal deflection yields the most accurate estimate of the probabilities that drivers will accelerate or decelerate. Based on these probabilities, a driving simulator was linearly pre-positioned in combination with a classical washout algorithm. The perceived motion incongruence was subjectively evaluated by the drivers (N = 34), testing: (i) no pre-positioning, (ii) pre-positioning, and (iii) pre-positioning with an increased longitudinal classical washout gain enabled by the pre-positioning. Results show that the pre-positioning improves the margins with respect to the longitudinal workspace limits (better workspace management), without affecting the motion incongruence ratings. When using the increased margins to increase the longitudinal gain, however, no significant reduction in motion incongruence ratings was observed. This is likely due to the small motion space of the hexapod motion system used in the current study. However, this paper shows that longitudinal driving behaviour can be
accurately predicted and can enable improved workspace utilization for driving simulators.},
keywords = {},
}

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TY  - CONF
TI  - Probabilistic Prediction of Longitudinal Driving Behaviour for Driving Simulator Pre-Positioning
AU  - Eppink, Jesse
AU  - Kolff, Maurice
AU  - Venrooij, Joost
AU  - Pool, Daan M. 
AU  - Mulder, Max
C1  - Antibes Juan-les-Pins, France
C3  - Proceedings of the Driving Simulation Conference 2023 Europe VR
DA  - 2023/09/08
PY  - 2023
SP  - 119
EP  - 126
LA  - en-US
PB  - Driving Simulation Association
SN  - 978-2-9573777-3-2
L2  - https://proceedings.driving-simulation.org/proceeding/dsc-2023/probabilistic-prediction-of-longitudinal-driving-behaviour-for-driving-simulator-pre-positioning
ER  - 

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